Based on the above assumption, we hold many instances for each relation type and directly use these instances to model the subspace of a relation type.

we represent an instance y of ith type as the linear combination of training instances associated with ith type: i;2 + ::: i;2 + ::: for some scalars , with j = 1, 2, …,ni.


This section describes how to estimate and exploit the semantic consistency for relation extraction.

given y's semantic decomposition: ::: ² we observe that if instance y locates at a semantic consistent region, then all its information will concentrate on a specific component yi, with all other components equal to zero vector 0.


To exploit such redundancy for more confident extraction, this paper first combines the evidence from different instances by combing their underlying components.

given the matched m instances Y={y 1 , y 2 , …, y m } for an entity pair (e1, e2), we first decompose each instance as y j = y j 1 + ::: + y j k + ² y j = y j 1 + ::: + y j k + ² , then the entity-pair level decomposition y = y 1 + ::: ::: + y k + ² is obtained by summarizing semantic components of different instances: Finally, the likelihood of an entity pair expressing ith relation type is scored as: is a score used to encourage extractions with more matching instances.


In this paper, we select informative training instances by seeking a most compact subset of instances which can span the whole subspace of a relation type.

all instances of ith type can be represented as a linear combination of these selected instances.


This is a classical K-Nearest-Neighbor classifier baseline.

given an entity pair, we first classify each matching instance using the labels of its 5 (tuned on training corpus) nearest neighbors with cosine similarity, then all matching instances' classification results are added together.


The results of our analyses also demonstrate a clear interaction between subjectivity and concreteness scores for nouns attributed by human raters.

objective adjectives are more likely to modify concrete nouns and subjective adjectives are more likely to modify abstract nouns.


This paper targets such types of implied relationships in textual inference scenarios.

we investigate the setting of Recognizing Textual Entailment (RTE) as a typical scenario of textual inference.


Third, the high frequency of metaphorically used language implies that it is hard to set a boundary beyond which a word is used metaphorically.

it seems clear that 18.5% of the BNC is not highly metaphoric, but rather is the sort of slightly metaphoric language that speakers are not consciously aware of because it is used so frequently.


To reach this goal, we adopt a max-margin framework (Socher et al., 2010;Socher et al., 2011a;Socher et al., 2013) for training.

suppose we have a training data like (u i , G(u i ), A(u i )), where u i is the input source sentence, G(u i ) is the gold derivation set containing all gold derivations of u i 1 , and A(u i ) is the possible derivation set that contains all possible derivations of u i .


The key difference between bottom-up parsing and MT decoding is that in parsing the gold tree for each input sentence is unique, while in MT many derivations can generate the same reference translation.

the gold derivation to update towards is a latent variable.


Before parsing starts, a preprocessing step is used to first attach the paired punctuations as properties of their neighbouring words, and then remove them from the sentence.

unigram for p in β0, β1, β2, β3, σ0, σ1, σ2 ppunc for p in β0, β1, β2, σ0, σ1 ppunc pw, ppunc pt bigram for p, q in (σ0, β0), (σ0, β1), (σ0, β2), (σ0, σ1), (σ0, σ2) ppunc qpunc, ppunc qt, ppunc qw for p, q in (σ2, σ0), (σ1, σ0), (σ2, σ0) ppunc qt, ppunc pt qt for p, q in (σ2, σ0), (σ1, σ0), (σ0, β0) dpq ppunc pt qt Table 3: Feature templates.


For example, the distance features in line 5 of Table 3 is used to capture the pattern that if a word w with comma property is the left modifier of a noun or a verb, the distance between w and its lexical head is often larger than 1.


Table 5 also lists the accuracies of stateof-the-art transition-based parsers.

"Huang 10" and "Zhang 11" denote Huang and Sagae (2010) and Zhang and Nivre (2011), respectively.


Several researchers have made their trained representations publicly avail- able, which we use directly in our experiments.

we use the SENNA embeddings of Collobert et al.


Our algorithm is motivated by the intuition that the diversity of images returned for a particular concept depends on its concreteness (see Figure 1).

we anticipate greater congruence or similarity among a set of images for, say, elephant than among images for happiness.


To tackle this, we first employ Google Translate 2 , a state-of-the-art machine translation system, to gain the translation of an event instance, similar to what has been widely done by previous studies in bilingual classification tasks e.g., Wan (2008); Then, we uniformly represent each text with bilingual word features.

we augment each original feature vector into a novel one which contains the translated features.


In bilingual classification, we represent a sample with bilingual features, which makes it possible to train with the data from two languages.

to achieve this goal, we employ a single feature augmentation strategy to augment the monolingual features into bilingual features, i.e., a sample x is represented as follows:  Where the tokens with the 'c'/'e' subscript mean the features generated from the Chinese/English text.


From this figure, we can see that Bilin-gual_location apparently outperform Monolingual, which verifies the effectiveness of using bilingual corpus.

the improvement by our approach in Chinese is impressive, reaching 7.6%.


This is understandable because the size of English data is much larger than that of Chinese data, 5285 vs. 2710, as shown in Table 2.

after checking the results in each subtype, we find that some subtypes in Chinese have very few samples while corresponding subtypes in English have a certain number samples.


For example, arbitrary translation pair such as (Jackie Chan, 成龙) can be obtained, if he is connected to his film 'Drunken Master' (醉拳) in both graphs.

we can propagate the seed translation similarity of (Drunken Master, 醉拳) to neighbor entities 'Jackie Chan' and '成龙' in each graph.


Therefore, we leverage relation temporality, which is both orthogonal and complementary to existing efforts leveraging entity temporality (Kle-mentiev and Roth, 2006;Kim et al., 2012;You et al., 2013).

we discover three new challenges on using temporality for relation understanding in comparable corpora, which we discuss in detail in Section 3.2.


Knowing a few seed entity translations using T N , 'Bruce Willis'→'布鲁斯•威利斯' and 'The Sixth Sense'→'第六感', we can find star in and 主演 are semantically similar.

we quantify this similarity based on the number of such common entity pairs that we denote as |H(r E , r C )| for an English relation r E and a Chinese relation r C .


• [C3] f EX : The existence of a common entity pair using the seed entity translations (boolean).

f EX = 1 if |H(r E , r C )| ≥ 1, and Additionally, we use the following features to consider absolute frequencies f req(•) of textual elements as well because 1) we are more confident with more evidence and 2) in the comparable corpora, the equivalent elements are likely to show similar frequencies.


We normalize the obtained function values for each English relations using the top-k Chinese translations.

for (r E , r C ), we redefine the score as where is the i-th rank Chinese relation for r E by Equation 2.


However, in some cases like ratify, highly related relations may induce noise.

we always 讨论 (discuss) before we 批 准 (ratify) something and hence the temporal behavior of 讨论 (discuss) is also very similar to that of ratify.


Our proposed approach LF+EF shows higher F1score than the baselines.


Many types of features were employed, including word length, sentence length, paragraph length, document length, sentence complexity, punctuation and capitalization, cognates, dependency parses, topic models, word suffixes, collocations, function word ngrams, skip-grams, word networks, Tree Substitution Grammars, string kernels, cohesion, and passive constructions (Abu-Jbara et al., 2013;Li, 2013;Brooke and Hirst, 2013;Cimino et al., 2013;Daudaravicius, 2013;Goutte et al., 2013;Henderson et al., 2013;Hladka et al., 2013;Bykh et al., 2013;Lahiri and Mihalcea, 2013;Lynum, 2013;Malmasi et al., 2013;Mizumoto et al., 2013;Nicolai et al., 2013;Popescu and Ionescu, 2013;Swanson, 2013;Tsvetkov et al., 2013).

word n-gram features appear to be particularly effective, as they were used by the most competitive teams, including the one that achieved the highest overall accuracy (Jarvis et al., 2013).


For comparison, we attempt to quantify the effect of removing the same number of randomlyselected words from the training data.

we discard all tokens that correspond to 100 word types that have the same or slightly higher frequency as the discriminative words.


The inter-fold overlap is even greater, with 19 out of 20 bigrams appearing in each of the 10 folds.

the bigrams fr and bu can be traced to both the function words from and but, and the presence of French and Bulgarian in I1.


Although the improvement does not seem large, our Figure 3 Performance of negative transfer detection vs. co-training algorithm shows a different behavior in that it can continue to make use of available training data to improve the system performance.

we do not need to identify the tipping point where the performance degradation can occur when more training samples are used.


Despite these advantages and promising results, there is a major limitation in NMT compared to the existing phrase-based approach.

the number of target words must be limited.


This limitation motivated us to develop an annotation model that includes the complete alignments between the source and the target sentences, which is straightforward to obtain since the complete alignments are available at training time.

we return to using only a single universal unk token.


Rare word translation is challenging for neural machine translation systems as shown in Figure 5.

the translation quality of our model before applying the postprocessing step is shown by the green curve, and the current best NMT system (Sutskever et al., 2014) is the purple curve.


This particular "tag" will be activated in a parameterized way during the training for predicting the target words.

the supervised signal from the words to predict will find, through layers of back-propagation, the importance of the tag bit in the "affiliated words" in the source language, and learn to put proper weight on it to make tagged words stand out and adjust other parameters in tagCNN accordingly for the optimal predictive performance.


At the same time, we address the challenge of learning the large set of neural network parameters.

• We develop new Neural Network Features to model non-local translation phenomena related to word reordering.


We report the experiments for the NIST condition in Table 4.

we investigate the impact of deploying our new features (column "Feat") and demonstrate the effects of the tensor architecture (column "Tensor") and multitask learning (column "MTL").


We observe that PSL-Joint performs better than SeededLDA for all classes.

for CER-TIFICATE and QUIZ, PSL-Joint exhibits a marked increase in scores when compared to SeededLDA.


The first two examples illustrate that PSL can predict the subtle difference between LECTURE-CONTENT and QUIZ-CONTENT.

notable is the third example, which contains mention of both subtitles and audio, but the negative sentiment is associated with audio rather than subtitles.


It attempts to embed components of a KG into continuous vector spaces, so as to simplify the manipulation while preserving the inherent structure of the original graph.

given a KG, entities and relations are first represented in a low-dimensional vector space, and for each triple, a scoring function is defined to measure its plausibility in that space.


To make the embedding space compatible with the observed facts, we make use of the triple set O and follow the same strategy adopted in previous methods.

we define an energy function on each candidate triple (e.g.


(2012), which also takes into account the entity category information, but in a more direct manner.

given an entity e with its category label c e , we create a new triple ⟨e, Generalization, c e ⟩ and add it into the training set.


Modeling Contrasting Meaning Automatically detecting contrasting meaning has been studied in earlier work such as (Lin and Zhao, 2003;Mohammad et al., 2008;Mohammad et al., 2013).

as far as the embedding-based methods are concerned, PILSA (Yih et al., 2012) made a progress in achieving one of the best results, by priming LSA to encode contrasting meaning.


In this paper, when combining word-pair distances learned by different components of the contrasting inference layer, we use some top hidden layer(s) to provide a non-linear combination.

we use two hidden layers, which is able to express complicated functions (Bishop, 2006).


Second, we train SCE using only lexical resources but not corpus statistics, so as to explore the behavior of lexical resources separately (we will use the relatedness modeling component below to model distributional semantics).

we use antonym pairs in lexical resources to learn contrasting neighbors.


Hence in the original high-dimensional space, all antonyms of a given word w i have the same probability to be its contrasting neighbors.

d in Equation 1takes a binary score, with value 1 indicating an antonym pair and 0 not.


SCE considers both semantic contrast and closeness.

for a given word w i , we jointly force that in the embedding space, its contrasting neighbors and semantically close neighbors to be similar to those in the original uncompressed space.


In embedding, such dependency can be used to help measure the degree of contrast.

we use the skip-gram model  to learn the relatedness embedding.


We utilized the labeled antonym relations to obtain more contrasting pairs under the contrast hypothesis (Mohammad et al., 2013), by assuming a contrasting pair is related to a pair of opposites (antonyms here).

in WordNet, we consider the word pairs with relations other than antonym as semantically close.


The collective models extend the L models by adding edge potentials which encourage the stance labels to respect disagreement relationships along reply links.

every reply link between authors (for author-level models) or between posts (for post-level models) x 1 and x 2 is associated with a latent variable disagree(x 1 , x 2 ).


In the literature it is common to employ a feature-based linear model.

one defines a feature function φ : X → {0, 1} n that represents entity tuples in an n-dimensional binary feature space 2 , and the model has a weight vector for each class, In this section we propose a specific family of models for classifying entity tuples.


Based on the above analysis, we propose two new distributional models for word representation using both syntagmatic and paradigmatic relations.

we learn the distributional representations of words based on the text region (i.e., the document) that the words occur in as well as the surrounding words (i.e., word sequences within some window size).


Therefore, syntagmatic models tend to favor the use of larger text regions as context.

a document is often taken as a natural context of a word following the literature of information retrieval.


model architecture as shown in Figure 3.

the document is used to predict a target word, and the target word is further used to predict its surrounding context words.


In this way, we can see that the implementational basis of the PDC model is consistent with that of syntagmatic and paradigmatic models.

pDC can indeed capture both syntagmatic and paradigmatic relations by processing the right distributional information.


Therefore, the objective function of PV-DM can no longer decomposed as the PDC model as shown in Equation 4and (5).

although PV-DM leverages both paragraph and context information, it is unclear how these information is collected and used in this model.


We define all of the parameters for the stage of argument classification to be trained as θ = (E, P F 1 , P F 2 , EF, W, b, W S , b s ).

e is the word embedding, P F 1 and P F 2 are the position embedding, eF is the embedding of the event type, W and b are the parameter of the filter, W s and b s are all of the parameters of the output layer.


Moreover, in different stages of event extraction, we adopted different parameters in the DMCNN.

in the trigger classification, we set the window size as 3, the number of the feature map as 200, the batch size as 170 and the dimension of the PF as 5.


However, our method could obtain further better results on the condition of only using automatically learned features from original words.

compared to Hong's cross-entity, it gains 0.8% improvement on trigger classification F 1 and 5.2% improvement on argument classification F 1 .


This is because of the changes in the output formats for the ESF task from 2013 to 2014.

the 2013 specification requires separate entity and justification provenance fields, but the 2014 collapses these into a single relation provenance field.


Moreover, an interval step of 100 is applied between consecutive samples in order to avoid too strong coherence.

for tracking changes in probabilities resulting from attribute relations, we ran in the first stage a specific burn-in with only heads and trigger relations.


Our method attempts to predict exactly this information, as shown in Figure 1 (c), guessing the remaining syntactic constituents that will allow us to acquire a proper parse tree.

the method consists of two parts: First, we propose a method that trains a statistical model to predict future syntactic constituents based on features of the input segment (Section 4).


In this section, we focus specifically Algorithm 1 Prediction algorithm for following constituents It should be noted that using these trees in T2S translation models is not trivial because each estimated syntactic constituent should be treated as an aggregated entity representing all possibilities of subtrees rooted in such a constituent.

there are two problems: the possibility of reordering an as-of-yet unseen syntactic constituent into the middle of the translated sentence, and the calculation of language model probabilities considering syntactic constituent tags.


In this paper, we propose a unified framework to jointly model local contexts, global topics as well as their correlations for lexical selection.

• First, we present a context-aware topic model (CATM) to exploit the features mentioned above for lexical selection in SMT.


Following Han et al, (2012), we adapt the well-known Gibbs sampling algorithm (Griffiths and Steyvers, 2004) to our model.

we compute the joint posterior distribution of hidden variables, denoted by P (z,ẽ,ẽ |D), and then use this distribution to 1) estimate θ, φ, ψ and ξ, and 2) predict translations and topics of all documents in D. we derive the joint posterior distribution from Eq.


We recruit students to label the relevance of the candidate questions regarding to the queries.

for each type of language, we let three native students.


To train the word embedding using these two methods, we apply the same setting for their common parameters.

the count of negative samples r is set to 3; the context window size l is set to 5; each model is trained through 1 epoch; the learning rate is initialized as 0.025 and is set to decrease linearly so that it approached zero at the end of training.


In this paper, we introduce the multi-column convolutional neural networks (MCCNNs) to automatically analyze questions from multiple aspects.

the model shares the same word embeddings to represent question words.


The preprocess method presented in (Bordes et al., 2014a) was used to make FREE-BASE fit in memory.

we kept the triples where one of the entities appeared in the training/development set of WEBQUESTIONS or CLUEWEB extractions provided in (Lin et al., 2012), and removed the entities appearing less than five times.


Using these vector representations defined for questions and answers, we can compute the score for the question-answer pair (q, a).

the scoring function S (q, a) is defined as: answer type (1) where f i (q) and g i (a) have the same dimension.


We present the model in the left part of Figure 1.


The vector representation g 1 (a) is computed via g 1 (a) = is a binary vector which represents the presence or absence of every relation in the answer path, W p ∈ R dq×|R| is the parameter matrix, and |R| is the number of relations.

the embeddings of relations that appear on the answer path are averaged.


Because there may be more than one correct answers for some questions, we need a criterion to determine the score threshold.

the following equation is used to determine outputs: where m is the margin defined in Equation (4).


The salience score of a question word depends on how much the word affects the computation of question representation.

if a word plays more important role in the model, its salience score should be larger.


Cross-linguistic experiments In the crosslinguistic experiments, we learn a mapping from the semantic space of language A to the semantic space of language B, which can then be used for translating words outside the training set.

given the vector representation of a word in language A, we apply the mapping to obtain an estimate of the vector representation of its meaning in language B, returning the nearest neighbour of the mapped vector in the B space as candidate translation.


5 Cross-modal experiments In the cross-modal experiments, we induce a mapping from visual to linguistic space.

given an image, we apply the mapping to its visual vector representation to obtain an estimate of its representation in linguistic space, where the word associated to the nearest neighbour is retrieved as the image label.


It has proven beneficial for both "deep" (Krizhevsky et al., 2012;Zeiler and Fergus, 2014) and "shallow" (Chatfield et al., 2014) systems, and it was recently introduced to NLP tasks (Zhang and LeCun, 2015).

in order to train the mapping function using both annotated data and points that are representative of the full search space, we rely on a form of data augmentation that we call visual chimera creation.


a first step towards engineering the negative evidence exploited by this method, in the context of inducing cross-space mapping functions.

our idea is that, given a training instance x i , an informative negative example would be near the mapped vectorŷ i , but far from the actual gold target space vector y i .


Both chimera and the intruder methods are flexible, and we plan to explore them further in future research.

we want to devise more semantically-motivated methods to select chimera components and negative samples.


In this paper, we address this issue, and propose a joint model system for IE and KB completion in a statistical relational learning (SRL) setting (Sutton and McCallum, 2006;Getoor and Taskar, 2007).

we outline a system which takes as input a partially-populated KB and a set of relation mentions in context, and jointly learns: 1) how to extract new KB facts from the relation mentions, and; 2) a set of logical rules that allow one to infer new KB facts.


S1 and S2 are syntactically different, this is evident from their corresponding parse trees in Figure 1.

s1 and s2 differ in where their PPs attach.


In this setting the problem is cast as a dependency parser correction problem (Atterer and Schütze, 2007;Agirre et al., 2008;Anguiano and Candito, 2011).

given a dependency parse of a sentence, with potentially incorrect PP attachments, rectify it such that the prepositional phrases attach to the correct sites.


The types of features considered in our experiments are summarized in Table 2.

the choice of features was motivated by our empirically driven characterization of the problem as follows: ∀i : ∃sv i o; svo(n1, v i , n2) For q2; (butterf ly, has, spots) For q2; (butterf ly, can see, spots) Noun Semantic Categories Source: we found that for verb-attaching PPs, n2 is usually a role filler for the verb, e.g., the net fills the role of an instrument for the verb catch.


On the other hand, for noun-attaching PPs, one noun describes or elaborates on the other.


Our method can be seen as an example of tapping into a positive feedback loop for machine reading, which has only become possible in recent years due to the progress made by information extraction and knowledge base construction techniques.

using background knowledge from existing resources to read better in order to further populate knowledge bases with otherwise difficult to extract knowledge.


Lookahead Skip-node The second approximation, Lookahead Skip-node, is related to the observation that when growing a substructure, we do not have to confine the growth only towards ancestors, as DFS traversal already ensures iterative manner of computation.

the constraint v i is an ancestor of v k can be dropped: In addition to those features generated by Linear Skip-node in Figure 5(a), Lookahead Skip-node can generate additional tree substructures, shown in Figure 5(b).


We propose filtered projection focused specifically on improving the precision of projected labels.

for a pair of sentences s SL and s TL in the parallel corpus, we retain the semantic label l SL,i projected from w SL,i onto w TL,i if and only if it satisfies the filtering policies.


1 guarantees the increase of the set of labels, we need to select a resampling function to favor precision while improving recall.

we use the formula k(i) = max(k 0 − i, 0), where k 0 is sufficiently large.


The main idea of existing CLSA researches is to first build up the connection between the source and target languages to overcome the language barrier, and then develop an appropriate knowledge transfer approach to leverage the annotated data from the source language to train a sentiment classification model in the target language, either supervised or semi-supervised.

these approaches exploit and convert the knowledge learned from the source language to automatically generate and expand the pseudo-training data for the target language.


Its best result justifies our assumption.

sB-CN leverages both the Chinese training data translated from the labeled English data and the unlabeled Chinese data used for boosting.


Meanwhile, though semantic similarities across languages are captured during bilingual embedding learning process, sentiment information of text is ignored.

bilingual embeddings learned from unlabeled parallel corpora are not effective enough for CLSC because of a lack of explicit sentiment information.


The encoder and decoder structures allow us to learn a mapping within and across languages.

for a given document pair (x E , x C ), we can learn bilingual embeddings to reconstruct The sum of 5 losses is used as the loss function of bilingual embeddings: In the unsupervised phase, we have learned the bilingual embeddings, which could capture the semantic information within and across languages.


The framework of the proposed model is shown in Figure 1.

we use events as the basic processing units for candidate extraction.


We repeatedly merge existing chains by the four principles above until convergence.

we focus on content words only, including verbs, nouns and adjective words.


Given a set of candidate events CE, we extract all the sentences that contain the events.

we add two artificial words, S and E , to the start position and end position of all sentences, respectively.


We use the standard HG test dataset to evaluate our model, which consists of 500 articles from DUC-04 task 1 7 , where each article is provided with four reference headlines.

we use the first 100 articles from DUC-07 as our development set.


As mentioned above, events are a trade-off between sentences and phrases, avoiding sparsity and structureless problems.

our event-driven model can interact with sentences and phrases, thus is a light combination for two traditional models.


In most previous works in domain adaptation (Blitzer et al., 2006;Daumé III, 2007), observed data samples may vary but label space is invariant 1 .


As in general domain adaptation setting, we assume that the source domain has a sufficient amount of labeled data but the target domain has an insufficient amount of labeled data.

for each test or target domain, we only use 10% of the training examples to simulate data scarcity.


This paper introduces a novel matrix factorization (MF) approach to learn latent feature vectors for utterances and semantic elements without the need of corpus annotations.

our model learns the semantic slots for a domain-specific SDS in an unsupervised fashion, and carries out semantic parsing using latent MF techniques.


In recent years, this problem led to the development of unsupervised SLU techniques Chen et al., 2014b).

proposed a frame-semantics based framework for automatically inducing semantic slots given raw audios.


Combining with a knowledge graph propagation model based on word/slot relations, the trained SLU model estimates the probability that each semantic slot occurs in the testing utterance, and how likely each slot is domain-specific simultaneously.

the SLU model is able to transform the testing utterances into domain-specific semantic representations without human involvement.


First, we build a word pattern matrix F w with binary values based on observations, where each row represents an utterance and each column refers to an observed unigram.

f w carries the basic word vectors for the utterances, which is illustrated as the left part of the matrix in figure 1(b).


To estimate the parameters in (5), we create a dataset of ranked pairs from M in (4): for each utterance u and each observed fact f + = u, x + , where M u,x ≥ δ, we choose each word pattern/slot x − such that f − = u, x − , where M u,x < δ, which refers to the word pattern/slot we have not observed to be in utterance u.

we construct the observed data O from M .


Note that the scoring function is shared by all the factors.

given the current decision function F m−1 , we can consider line 3 to line 9 a process of generating the pseudo Algorithm 1 S-MART: A family of structured learning algorithms with multiple additive regression trees 1: F0(x, y k ) = 0 2: for m = 1 to M do: going over all trees 3: for all examples do: going over all examples 5: for y k ∈ Ω(x) do: going over all factors 6: For all u k , obtain g ku by Equation (3)  7: end for 9: end for 10: Fm(x, y k ) = Fm−1(x, y k ) + hm(x, y k ) 12: end for training data D for modeling the regression tree.


Instead of defining a more complicated structure and learning everything jointly, we employ a two-stage approach as the solution for modeling entity-entity relationships after we found that S-MART achieves high precision and reasonable recall.

in the first stage, the system identifies all possible entities with basic features, which enables the extraction of entity-entity features.


It is motivated by a key application of entity linking -retrieval of relevant tweets for target entities, which is crucial for downstream applications such as product research and sentiment analysis.

given a query entity we can search for tweets based on the match with some potential surface forms of the query entity.


Note the tuning is only conducted for the purpose of robust evaluation.

we adopt a simple tuning strategy that works well for all the algorithms, in which we add a bias term b to the scoring function value of Nil: We choose the bias term b from values between −3.0 to 3.0 on the dev set and apply the same bias term at test time.


Initially, all the decision variables y are set to zero.

each factoid node is regarded as forming its own factoid hierarchy, as illustrated in Figure 2(a).


In this part, we demonstrate that the introduction of relevant knowledge could be helpful to real NLP applications.

we choose the document classification task as a demonstration, which aims to classify documents into predefined categories automatically (Sebastiani, 2002).


To do this in a natural, pragmatically interpretable way, we must not only derive answers like in (3), but crucially, also rule out infelicitous responses like the following, where a logically possible alternative leads to incoherence due to the low probability of an appropriate requirement like {GARDEN, BASE-MENT}.

(wanting a garden has little effect on the probability of wanting a basement.)


's method to tag each sentence of our essays with an argument label, but modify their method to accommodate differences between their and our corpus.

our more informal corpus # Rule 1 Sentences that begin with a comparison discourse connective or contain any string prefixes from "conflict" or "oppose" are tagged OP-POSES.


To generalize our feature set in a way that does not risk introducing prompt-dependent features, we introduce POS n-gram features.

we construct one feature from each sequence of 1−5 part-of-speech tags appearing in our corpus.


There are 14 such lists, which we use to generalize transitional features.

we construct a feature for each of the 14 phrase type lists.


While most prior work discussed above focuses on the underlying set of documents, (e.g., by clustering documents), we focus directly on the topics.

we formulate the problem as subset selection on the set of topics within a DAG while simultaneously considering the documents to be categorized.


Hence, While our problem is closely related to clustering approaches, which consider the set of documents directly, there are some crucial differences.

we focus on producing a clustering of documents where clusters are encouraged to honor a pre-defined DAG structured topic hierarchy.


In this paper we propose a method for explaining the relationship between two entities, which we evaluate on a newly constructed annotated dataset that we make publicly available.

we consider the task of explaining relationships between pairs of Wikipedia entities.


A binary indicator x, which is generated by a Bernoulli distribution π x drawn from a Beta distribution with symmetric hyperparameter γ x , determines whether a word is generated by ψ z or ψ e .

if x = 0, a word is drawn from ψ z ; otherwise the 3 http://en.wikipedia.org/wiki/July 2009 Urumqi riots Figure 6: The temporal distribution of documents about the Urumqi riots, which can be described by a Gaussian distribution (the dashed curve).


We propose to use learning-to-rank techniques to select the most salient events to generate the final chronicle since we believe the reference event chronicle can teach us the principles of selecting salient events.


According to the reference sport event chronicle, however, only matches after quarterfinals in a tournament are qualified to be event entries.

a sports chronicle should provide information about the results of semi-final and final, and the champion of the tournament instead of the first-round match's result, which accounts for the poor performance.


We can see that our approach achieves significantly better performance than the baseline approaches.

it can verify the mentions of newly emergent morphs.


Our contention, and the main motivation behind our work, is that we can do better by leveraging explicit mechanisms adopted by authors in keyphrase generation.

we focus on a tendency to expand keyphrases by adding terms, coupled with a pressure to abbreviate to retain succinctness.


Nguyen et al., (2007) found that they could produce better keyphrases by extending existing models (Frank et al., 1999) to include an acronym indicator as a feature.

if a candidate phrase had an associated parenthetical acronym associated with it in the text a binary feature would be set.


Though the idea that abbreviations and keyphrases are linked fits within our understanding of scientific writing, we confirmed our intuition through a small experiment.

we looked at the 85 unique keyphrases (in this case, article titles) listed in the Wikipedia entry for List of Machine Learning Concepts (Wikipedia, 2014).


We found that the cause was a large set of "bad" keyphrases.

69.3k (69.5%) of author-defined keyphrases (occurring in ACMCORPUS but not in AB-BREVCORPUS) are used as a keyword in only one paper.


To test this systematically, we calculated semantic similarity between each keyphrase in the WikiCorpus dataset to "computer science."

we utilize Explicit Semantic Analysis (Gabrilovich and Markovitch, 2009) to calculate similarity.


It is interesting to consider if edges which are derived by "containment" reflect a temporal pattern.

if keyphrase A EXTENDS B, does the first mention of A in the literature happen after B?


Compared to standard RBMs, a crucial difference is that hidden units now have a heterogeneous structure instead of being homogeneous as in the standard basic RBM model.

we rely on three types of hidden units, representing aspect, sentiment, and background, respectively.


For the experimental setup, we use ten hidden units in our Sentiment-Aspect Extraction RBM (SERBM), where units 0-6 capture aspects, units 7-8 capture sentiment information, and unit 9 stores background information.

we fix hidden units 0-6 to represent the target aspects Food, Staff, Ambience, Price, Ambience, Miscellaneous, and Other Aspects, respectively.


It is worth noting that besides the approach difference, these two works employ rather different resources and settings in their evaluation, making it impossible to directly compare their performance.

while Plank and Moschitti (2013) only use the path-enclosed trees induced from the constituent parse trees as the representation for relation mentions, Nguyen and Grishman (2014) include a rich set of features extracted from multiple resources such as constituent trees, dependency trees, gazetteers, semantic resources in the representation.


For the feature-based method, we utilize all the features extractable from the PET trees that are standardly used in the state-of-the-art featurebased systems for DA of RE (Nguyen and Grishman, 2014).

the feature set employed in this paper (denoted by FET) includes: the lexical features, i.e., the context words, the head words, the bigrams, the number of words, the lexical path, the order of mention (Zhou et al., 2005;Sun et al., 2011); and the syntactic features, i.e., the path connecting the two mentions in PET and the unigrams, bigrams, trigrams along this path (Zhou et al., 2005;Jiang and Zhai, 2007a).


In this section, we first give the intuition that guides us in designing the proposed methods.

one limitation of the syntactic semantic tree kernel presented in Plank and Moschitti (2013) ( §2.1) is that semantics is highly tied to syntax (the PET trees) in the kernel computation, limiting the generalization capacity of semantics to the extent of syntactic matches.


TREE: This is motivated by the training of recursive neural networks (Socher et al., 2012a) for semantic compositionality and attempts to aggregate the context words embeddings syntactically.

we compute an embedding for every node in the PET tree in a bottom-up manner.


SIM: Finally, for completeness, we experiment with a more obvious way to introduce word embeddings into tree kernels, resembling more closely the approach of Plank and Moschitti (2013).

ly, the SIM method simply replaces the similarity scores between word pairs obtained from LSA by the cosine similarities between the word embeddings to be used in the SSTK kernel.


In this section, we examine the semantic representation for DA of RE in the tree kernelbased method.

we take the systems using the PET trees, word clusters and LSA in Plank and Moschitti (2013) as the baselines and augment them with the embeddings WED = HEAD+PHRASE.


In this work we propose an effective method to solve the problem of finding counterpart terms across time.

for an input pair of a term (e.g., iPod) and the target time (e.g.


The key difficulty comes from the change of the entire context that results in low overlap of context across time.

it is difficult to find temporal counterpart terms by directly comparing context vectors across time.


The key idea is that the relative positions of words in each vector space should remain more or less stable.

a temporal counterpart term should have similar relative position in its own vector space as the position of the queried term in the base time space.


A Boolean feature is taken to represent the morpheme information.

the characters which appear more than once within different cues in training corpus were selected as the features.


In this paper we propose a new framework using the sub-structures of dependency tree as scope candidates.

given a cue, we adopt the following heuristic rule to get the scope candidates in the dependency tree.


Another difference is that PRA is very limited in terms of expressiveness.

inductive logic programming The first rule includes SportsTeam as a constant, corresponding to a particular graph node, which is a the semantic class (hypernym) of the target node t. The second rule simply assigns NFL as the target node for the AthletePlaysForTeam relation; if used probabilistically, this rule can serve as a prior.


It is shown that the proposed extensions allow one to effectively explore a larger feature space, significantly improving model quality over previously published results in both domains.

incorporating paths with constants significantly improves model quality on both tasks.


Such path types may be indicative of an extended relational meaning between graph nodes that are linked over these paths; for example, the path AtheletePlaysForTeam, TeamPlaysInLeague implies the relationship "the league a certain player plays for".

pRA encodes p (s → t; π j ), the probability of reaching target node t starting from source node s and following path π j , as a feature that describes the semantic relation between s and t. provided with a set of selected path types up to length , p = {π 1 , .


This task is implemented in the graph as a query, where we let the query distribution be uniform over the given seeds (and zero elsewhere).

our goal is to find target nodes that are related to the query nodes over the relation r =similar-to, or, coordinate-term.


For N-to-N relations in predicting both head and tail, our approach improves the Hits@10 by almost 7.4% than CTransR.

for Table 5: Experimental results on FB15K by mapping properities of relations (%).


2 In the rest of this section, however, we wish to challenge the use of IAA statistics in GEC and question their value in this field.

while IAA statistics may be informative in areas where items can be classified into single, welldefined categories, such as in part-of-speech tagging, we argue that they are less well-suited to GEC and SMT, where there is often more than one correct answer.


In a related study, and aware of the fact that there are often multiple ways to correct individual words in sentence, Rozovskaya and Roth (2010) instead chose to compute agreement at the sentence level.

three raters were asked simply to decide whether they thought 200 sentences were correct or not.


In addition to calculating scores on a human vs human basis, we also calculated the F-scores for the top three CoNLL-2014 teams, AMU (Junczys-Dowmunt and Grundkiewicz, 2014), CAMB (Felice et al., 2014), and CUUI (Rozovskaya et al., 2014), versus all the combinations of humans (Equation 3).

s ∈ S, where S is the set of all three shared task systems, i.e., {AMU, CAMB, CUUI}, and f (s, X) is the same function in Equation 1 which is the score computed by the M2 scorer to evaluate system s against the set of annotators X chosen to be in the gold standard.


More importantly, however, we notice that some error types achieve much higher scores than others, which suggests some annotators agree on certain categories more than others.

noun number (Nn) and subjectverb agreement (SVA) errors achieve the highest scores, at just under 90% F 0.5 , which is also not far from the 100% F 0.5 that would be achieved if we had gold standard answers for all possible alternative corrections of this type.


(2014) to compare each team's performance on different error types in the CoNLL-2014 shared task.

while the overall relationship between the system and human scores on 10 and 50 essays remains more or less the same, researchers must be aware that smaller datasets may have more skewed error distributions, which in turn may affect system performance, dependent upon correction strategy.


as paronomasia and syllepsis, or more generally as puns, in which homonymic (i.e., coarse-grained) lexical-semantic ambiguity is a deliberate effect of the communication act.

the writer intends for a certain word or other lexical item to be interpreted as simultaneously carrying two or more separate meanings, or alternatively for it to be unclear which meaning is the intended one.


Moreover, supervised approaches tend to be such that they can disambiguate only those words for which they have seen sufficient training examples to cover all senses.

most of them cannot disambiguate words which do not occur in the training data, nor can they select the correct sense of a known word if that sense was never observed in the training data.


we do not assume the availability of any labeled data for learning word representations.


Experimental results for cross-domain sentiment classification on a benchmark dataset show that the word representations learnt using the proposed method statistically significantly outper-form a state-of-the-art domain-insensitive word representation learning method (Pennington et al., 2014), and several competitive baselines.

our proposed cross-domain word representation learning method is not specific to a particular task such as sentiment classification, and in principle, can be in applied to a wide-range of domain adaptation tasks.


2014proposed a method that jointly performs WSD and word embedding learning, thereby learning multiple embeddings per word type.

the number of senses per word type is automatically estimated.


1 requires that a non-pivot w that co-occurs with a pivot c in the document d is assigned a higher ranking score as measured by the inner-product between c S and w S than a nonpivot w * that does not occur in d. We randomly sample k non-pivots from the set of all source do-main non-pivots that do not occur in d as w * .

we use the marginal distribution of non-pivots p(w), estimated from the corpus counts, as the sampling distribution.


When we use language, we take demographic factors of the speakers into account.

we do have certain expectations as to who uses "super cute," "rather satisfying," or "rad, dude."


In this section, we describe how a notion of word order is included in the model through the integration of an n-gram language model.

equation 5can be represented as the product of two factors: where ∆ is the semantic component that scales p(w) in function of the context.


In figure 1, we see that S Semantics is poorly fit by a linear function.


Table 6 shows generally larger effects for native than non-native speakers.

the interaction between duration estimates and word frequencies, and semantic surprisal were not significant predictors in the non-native speaker model (however, random slopes for semantic surprisal under speaker still improved model fit very strongly, showing that non-native speakers differ in whether and how they take into account semantic surprisal during language production).


From a language science or psycholinguistic per-spective, we have shown that semantic surprisal affects spoken word durations in natural conversational speech, thus providing additional supportive evidence for the uniform information density hypothesis.

we find evidence that UID effects connect linguistic levels of representation, providing more information about the architecture of the human processor or generator.


In SBTDM, the parameters are instead sampled from particular SBT priors.

the generative model is: The variable φ represents the distribution of topics given words, P (Z|W ).


Remarkably, an alternative approach is used in whole-sentence maximum entropy (WSME) language modeling (Rosenfeld et al., 2001).

a WSME model has the form: Here f (x) is a vector of features, which can be arbitrary computable functions of x, λ is the corresponding parameter vector, and Z is the global normalization constant.


The algorithm can be obtained by combining the standard SA algorithm for training single random fields (Gu and Zhu, 2001) and a trans-dimensional extension of the self-adjusted mixture sampling algorithm (Tan, 2015).

consider the following joint distribution of the pair (l, x l ): where π l is set to n l /n for l = 1, .


Different types of features, like the skipping features, the higher-order features and the crossing features can all be easily supported in TDRF LMs, and the performance is improved to varying degrees.

the TDRF using the "w+c+ws+cs+cpw" features with class number 200 performs comparable to the RNNLM in both perplexity and WER.


But in this case, there is no need to introduce ζ l , because the normalizing constants Z l (λ) are canceled out as seen from (7).

the learning rate γ λ and the L 2 regularization are configured the same as in TDRF training.


Despite recent progress, fitting RFs of moderate or large dimensions remains to be challenging (Koller and Friedman, 2009;Mizrahi et al., 2013).

the work of (Pietra et al., 1997) is inspiring to us, but the improved iterative scaling (IIS) method for parameter estimation and the Gibbs sampler are not suitable for even moderately sized models.


We can exploit these findings to avoid the naive approach to draw a sample from (1).

we compute the document-specific sparse term exactly and for the remainder language model term we borrow idea from (Li et al., 2014).


Moreover, the task of building the alias tables can be outsourced to other cores.

with the combination of both Cholesky and Alias tricks, the sampling complexity can thus be brought down to O(K d M 2 ) where K d represents the number of actually instantiated topics in the document and K d K. we plot the sampling rate achieved naively, with Cholesky (CH) trick and with Cholesky+Alias (A+CH) trick in figure 2 demonstrating better likelihood at much less time.


In this section we evaluate our Word Vector Topic Model on various experimental tasks.

we wish to determine: • Is our model is able to find coherent and meaningful topics?


This work has connections with the ranking-based approaches for learning to reproduce human judgments of MT quality.

our setting is similar to that of Duh (2008), but differs from it both in terms of the feature representation and of the learning framework.


One possible way to do this is to introduce a task-specific cost function that penalizes the disagreements similarly to the way Kendall's τ does.

4 we define a new Kendall cost as follows: where we use the sigmoid function sig as a differentiable approximation to the step function.


Note that the specific values for γ and β are not really important, as long as they are large.

in our experiments, we used γ = β = 100.


We have presented a novel framework for learning a tunable MT evaluation metric in a pairwise ranking setting, given pre-existing pairwise human preference judgments.

we used a neural network, where the input layer encodes lexical, syntactic and semantic information from the reference and the two translation hypotheses, which is efficiently compacted into relatively small embeddings.


The target tree fragments of ζ will replace the respective linked leaves in the target tree fragments of the sentential form ξ.

substitution has to respect the symbols in the linked target tree fragments and all linked leaves are replaced at the same time.


The rule extraction of Maletti (2011) extracts minimal tree-to-tree rules, which are rules containing both source and target tree fragments, from sentence pairs of a word-aligned and bi-parsed parallel corpus.

this requires parses for both the source and the target language sentences which adds a source for errors and specificity potentially leading to lower translation performance and lower coverage (Wellington et al., 2006).


Similarly, non-minimal rules are those that can be obtained from minimal rules by substitution.

each lexical item of a sentence pair occurs in exactly one minimal rule extracted from that sentence pair.


For example, GN has a comparable number of hidden nodes with TLayer 200 , but only has half of its parameters and takes about 70% time to train in each iteration.

our proposed network structure provides more efficient training in these cases and achieve better results.


EMIT actions are not included as they always implicitly occur at the end of each fragment.

the features, encoded with the "one-hot" encoding are: These features are also conjoined with the quantized signed distance (in the original sentence) between each pair of words.


The core components of KRISP are string-kernel classifiers P (r, i..j|E) denoting the probability that a production r in the AST covers the span of words i..j in the sentence E. Here, i < j are positions in the sentence indicating the span of tokens most relevant to this production.

the substring E[i..j] denotes the production r with probability P (r, i..j|E).


In other words, the substring E[i..j] denotes the production r with probability P (r, i..j|E).

the probability of a semantic derivation D is defined as follows: we assume that each production is independent of all others, and is conditioned only on the string to which it is aligned.


For both GeoQuery and Jobs corpus, our recall is significantly higher than existing systems because meanings discovered by NL2KRs learning algorithm is more general and reusable.

meanings learned from a particular sentence are highly likely to be applied again in the context of other sentences.


Thirdly, we demonstrate experimentally, in Section 5, that multiple many-tomany alignments may be an extremely useful first step in boosting the performance of a G2P model.

we show that by conjoining a base system with additional systems very high performance increases can be achieved.


Moreover, assume that a set length s N may be matched up with each other.

s defines the types of valid 'many-to-many match-up operations'.


We now describe two sets of experiments, a controlled experiment on the Combilex data set where we can design our offline/black box systems ourselves and where the black box systems are trained on a similar distribution as the baseline and the extended baseline systems.

the black box systems operate on the same output alphabet as the extended baseline systems, which constitutes an 'ideal' situation.


Concerning differences in alignments between the two alignment types, exact vs. approximate, an illustrative example where the approximate model fails and the exact model does not is ('false' alignment based on the approximate model indicated): r ee n t e r e d r i E n t @' r d r i E n t @' r d which nicely captures the inability of the approximate model to account for correlations between the matched-up subsequences.

while the segmentations of the three shown sequences appear acceptable, a matching of graphemic t with phonemic n, etc., seems quite unlikely.


Finally, another important contribution of our work is to outline an 'approximation algorithm' to inducing multiple many-to-many alignments of strings, which is otherwise an NP-hard problem for which (most likely) no efficient exact solutions exist, and to investigate its suitability for the problem task.

we have seen that exact alignments lead to better overall model performance, but that the margin over the approximation is not wide.


In this study, we propose new methods to effectively integrate information of OOV words and their normalization for the NER task.

by adopting joint decoding for both NSW detection and NER, we are able to outperform stateof-the-art results for both tasks.


We extract linguistic features for our model from the BNC.

we parse the corpus using the RASP parser (Briscoe et al., 2006) and extract subject-verb and verb-object relations from its dependency output.


Experimental results shows that capturing the predicate interaction and case interaction is effective for Japanese PAS analysis.

implicit (Zero) argument identification, one of the problematic issues in Japanese PAS analysis, is improved by taking such interactions into account.


Note that this is a different metric than the precision/recall of prior work on alignments, and is based on both a different alignment dataset and subtly different alignment annotation scheme.

we require that every AMR node aligns to some token in the sentence, which forces the system to always align nodes, even when unsure.


A frame node represents a single clause (e.g., "change the channel to a movie") and has at most one successor node.

a frame node consists of a verb ν (e.g., arrange, collect), a set of object descriptions {ω i } which are the arguments of the verb (e.g., the guinness book, movie channel), and spatial relations r between the arguments (e.g., between, near).


We use planner and a simulator that to-gether specify a deterministic mapping from the current environment e i and a logical form z i to a new environment e i+1 .

the planner takes the current environment e i and a logical form z i and computes the action sequence a i = planner(e i , z i ) for achieving the post condition represented by z i .


Given a sequence of frame nodes c 1:k and an initial environment e 1 , our semantic parsing model defines a joint distribution over logical forms z 1:k .

we define a conditional random field (CRF) over z 1:k , as shown in Figure 2: is the feature vector and θ is the weight vector.


For efficiency, we used an objective similar to pseudolikelihood to estimate the parameters θ.

we maximize the average loglikelihood over each adjacent pair of logical forms underp θ : The weights were initialized to 0.


We consider two metrics, IED and END, which measure accuracy based on the action sequence and environment, respectively.

the IED metric  is the edit distance between predicted and true action sequence.


Since the dependencies between constituents can be an exponential number and representing structures in learning algorithms is rather challenging, automatic feature engineering through kernel methods (Shawe-Taylor and Cristianini, 2004;Moschitti, 2006) can be a promising direction.

in (Zanzotto and Moschitti, 2006), we represented the two evaluating sentences for the RTE task with syntactic structures and then applied tree kernels to them.


The relational structures seem to drastically reduce the inconsistent matching between positive and negative examples, reflecting in remarkable increasing in Precision.

sM sP T K LsA achieves the state of the art 6 , i.e., 84.1 (Madnani et al., 2012).


The extensive experimentation using many kernel models also combined with traditional feature vector approaches sheds some light on how engineering effective graph and tree kernels for learning from pairs of entire text fragments.

our best models significantly outperform the state of the art in PI and the best kernel model for RTE 3, with Accuracy close to the one of the best system of RTE 3.


Some users favor giving higher ratings like 5 stars and some users tend to give lower ratings.

sentiment ratings from the same user are more consistent than those from different users.


We can find that: the absolute rating differences between two reviews written by a same user are lower than those written by different users (t-test with p-value < 0.01).


We use CNN (Kim, 2014;Kalchbrenner et al., 2014) in this work as it does not rely on external parse tree.

we use multiple convolutional filters with different widths to produce sentence representation.


Noun phrase (NNP) extraction and sentiment inference are performed automatically via state of the art NLP tools.

we use the Stanford Sentiment Analysis tool (Socher et al., 2013) to infer sentiment.


We report the interaction features that are the best predictors of verbal irony in the respective subred-  dits (for both polar community pairs).

we estimated the weights for every interaction feature using the entire training dataset, and repeated this process 100 times to account for variation due to the SGD procedure.


In this paper, we address above two challenges by incorporating the label dependence among the emotion labels and the context dependence among the contextual instances into a factor graph model.

we recast sentence-level emotion classification as a factor graph inferring problem in which the label and context dependence are modeled as various factor functions.


For instance, in Figure  1, S1, S2, and S3, from the same paragraph, all share the emotion category joy.

we propose a factor graph, namely Dependence Factor Graph (DFG), to model the label and context dependence in sentence-level emotion classification.


 LabelD: As the state-of-the-art approach to handling multi-label classification, this approach incorporates label dependence, as described in (Wang et al., 2014).

this approach first utilizes a Bayesian network to infer the relationship among the labels and then employ them in the classifier.


 DFG-label (Neighbor): Our DFG approach with the context dependence only.

the neighboring instances are considered as context.


 DFG-label (Paragraph): Our DFG approach with the context dependence only.

the instances in the same paragraph are considered as context.


 DFG-label (Document): Our DFG approach with the context dependence only.

the instances in the same document are considered as context.


In this work, we propose a dual-view co-training approach based on dualview BOW representation for semi-supervised sentiment classification.

we model both the original and antonymous reviews by a pair of bags-of-words with opposite views.


The training, bootstrapping and testing processes are all performed by observing two opposite sides of one review.

we consider not only how positive/negative the original review is, but also how negative/positive the antonymous review is.


Note that the class labels in two views are kept opposite: .

we reverse the class label in the original view (i.e., positive to negative, or vice versa), as the class label of the created antonymous view.


In this work, we design a dual-view co-training algorithm to bootstrap the class labels by a joint observation of two sides of one review.

we propose a new bootstrapping mechanism, based on a principle called dual-view sentiment consensus.


Given an unlabeled instance {x u o , x u a }, dual view sentiment consensus requires that, the original prediction y u o and the antonymous prediction should be opposite: y u a = 1 − y u o .

we only select the instances of which the original prediction is positive/negative, and the same time the antonymous prediction is negative/positive.


Given a test example x te with its original view denoted by x te o and antonymous view denoted by x te a , let p o (•|x te o ) be the posterior probability predicted by the original-view classifier h o , and p a (•|x te a ) be the posterior probability predicted by h a .

the dual-view testing process can be formulated as follows: the final positive score is assigned by measuring not only how positive the original review is, but also how negative the antonymous one is; the negative score is assigned by measuring not only how positive the original review is, but also how negative the antonymous one is.


In order to exploit this fact, semi-supervised learning methods can be used.

it is possible to derive word representations by exploiting word co-occurrence patterns in large samples of unlabeled text.


In this work, new features were estimated with a convex objective function that combined the log-likelihood of the training data, with regularization penalizing the Frobenius norm of the distortion matrix.

the matrix of the differences between the original and the new embeddings.


The structured skip-gram models the following probability: Here, w i ∈ {1, 0} v×1 is a one-hot representation of w = i.

a vector of zeros of the size of the vocabulary v with a 1 on the i-th entry of the vector.


We define the adapted embedding matrix as the factorization S • E, where S ∈ R s×e , with s e. We estimate the parameters of the matrix S using the labeled dataset, while E is kept fixed.

we determine the optimal projection of the embedding matrix E into a sub-space of dimension s. The idea of embedding sub-space rests on two fundamental principles: 1.


We also include two other approaches that are related to the one here proposed, where a neural network initialized with pre-trained word embeddings is used to learn relevant features.

we compare the following systems: • NRC (Kiritchenko et al., 2014), a support vector machine classifier with a large set of hand-crafted features, including word and character n-grams, brown clusters, POS tags, morphological features, and a set of features based on five sentiment lexicons.


Thus, we solve this problem by fixing all the embeddings, except for the word types not found in the pre-training corpus.

instead of leaving the unknown words as the zero vector, we use the labeled data to learn a better representation.


Most of the extensions of ROVER, such as iROVER (Hillard et al., 2007), cROVER (Abida et al., 2011) and the one described in (Zhang and Rudnicky, 2006), aim to learn a scoring function that allows improving the reordering of words inside each slot.

iROVER reorders the words in each slot by means of a classifier trained with features that characterize the individual ASR systems.


The second strategy relies on directly training a ranking model from a set of instances where S i and T j i respectively represent segments and transcriptions, and T R j i represents "true ranks" computed from the corresponding reference WER values T W j i .

given two transcriptions, T j i and T k i and the true WERs, then It is worth to note that MLR, differently from the two regression methods described above, performs a pairwise comparison between the segment candidates.


That is, given two transcriptions, T j i and T k i and the true WERs, then It is worth to note that MLR, differently from the two regression methods described above, performs a pairwise comparison between the segment candidates.

for each pair of segment transcriptions, the algorithm processes their corresponding feature vectors against each other and decides to place one transcription ahead of the other, as long as returning a score for this decision.


Extra-trees are a tree-based ensemble method for supervised classification and regression, which we successfully used in the past both for MT (de Souza et al., 2013) and ASR quality estimation   .

: " †" = the result is not statistically different from random ROVER; "•" = the result is not statistically different from SysO; " " the result is not statistically different from SegO.


Among all the possible combinations (8 factorial), our two best methods (RR2 and MLR) result in a systems' ordering with high correlation with the official IWSLT2013 ranking.

mLR achieves correlation of 0.905 with three out of eight systems (1, 2 and 8) that are correctly positioned.


However our long-term goal here is not on perfecting this basic multi-text generation scenario of reconstructing input documents, but rather on extending it to more important applications.

the autoencoder described in this work, where input sequence X is identical to output Y , is only the most basic instance of the family of document (paragraph)-to-document (paragraph) generation tasks.


Although this kind of parsers achieve state-of-theart performances (Bohnet, 2010), their predictions are limited to the phenomena that occur in the treebanks they are trained on.

they often fail at correctly distinguishing elements that are subcategorized by a verb (henceforth complements) from others (modifiers).


In this paper, we propose to automatically learn a set of effective features via neural networks.

we build a feedforward neural network model, which takes as input a few primitive units (words, POS tags and certain contextual tokens) from the local context, induces the feature representation in the hidden layer and makes parsing predictions in the output layer.


A parser state s ∈ S is defined as a tuple s = (σ, β), where σ is a stack which is maintained to hold partial subtrees that are already constructed, and β is a queue which is used for storing remaining unprocessed words.

the initial state has an empty stack σ and a queue β containing the entire input sentence, and the terminal states have an empty queue β and a stack σ containing only one complete parse tree.


Then, in the projection layer, each primitive unit is projected into a vector.

word-type units are represented as word embeddings, and other units are transformed into one-hot representations.


Different with standard RNN, there are no nonterminal nodes in dependency tree.

each node h in dependency tree has two associated distributed representations: 1. word embedding w h ∈ R m , which is denoted as its own information according to its word form; 2. phrase representation x h ∈ R m , which is denoted as the joint representation of the whole subtree rooted at h. when h is leaf node, Given a subtree rooted at h in dependency tree, we define c i , 0 < i ≤ L as the i-th child node of h, where L represents the number of children.


We employ a variant of max-violation (Huang et al., 2012) as our training objective, in which parameters are updated based on the worst mistake found during search, rather than the first mistake as performed in the early update perceptron algorithm (Collins and Roark, 2004).

given a training instance (w, y) where w is an input sentence and y is its gold derivation, i.e., a sequence of actions representing the gold parse tree for w, we seek for the step j * where the difference of the scores is the largest: Then, we define the following hinge-loss function: wherein we consider the subset of sub-derivations B j * ⊂ B j * consisting of those scored higher than ρ θ (y j * 0 ): Unlike Huang et al.


In this paper, we propose a new feature selection method that can efficiently select representative features in the kernel space to improve the quality of linear models.

given a limited number of basic features such as the commonly used unigrams and bigrams, our method performs feature selection in the space of their combinations, e.g, the concatenation of these n-grams.


each feature is the product of at most R basic features As we assume that all features are binary 1 , f j can be rewritten as the minimum of these basic features: f j = min{b j 1 , b j 2 , .


In this way, we could ignore these features without explicit calculation.

the objective function for learning L 1 norm SVMs is: where is the hinge loss function for the i-th sample.


Remind that our purpose is to keep the model sparse, and we would like to pick g j = 0 if possible.

we can keep e., the frequency of feature f j .


As mentioned above, each feature in polynomial kernel space is defined as Equivalently, we can define f j in a recursive way, which is more frequently used in the rest of the paper.


The minimum count c * provides the upper bound of the frequency.

when pruning elements with frequencies no greater than a predefined threshold θ, we could safely prune the element if c * ≤ θ.


We did not estimate #h + and #h − simultaneously, because this needs two bloom filters for positive and negative gradients respectively, which consumes too much memory.

in the first round, we initialize the spectral bloom filter so that all counters are set to zero.


This method starts with a set of trees consisting of single nodes, and then expands each subtree attaching a new node.

it first indexes the words in the preorder of the parse tree.


To overcome this time bottleneck, we compute features only for those analyses of every input sentence prefix that belongs to the kbest analyses at the end of the sentence.

π n is the set of only those prefix trees that are used by the k-best analyses at the end of the sentence.


We have shown that it is possible to push the precision on discontinuous constituents, but not the recall, to the level of what can be achieved with a grammar-based approach.

the outcome of the experiments involving the EXTENDED features and IMPOR-TANCE drives us to the conclusion that the major problem when parsing discontinuous constituents is data sparseness.


In every training iteration i, we randomly sample the training instances, and perform online learning with early update (Collins and Roark, 2004).

given a training example, we use beam-search to decode the sentence.


Zhang and Nivre (2012) find that global learning and beam search should be used jointly for improving parsing using a linear transition-based model.

increasing the beam size, the accuracy of ZPar (Zhang and Nivre, 2011) increases significantly, but that of MaltParser does not.


A linguist provided a paraphrase for each sampled sentence according to these instructions: The paraphrases should more or less convey the same information as the original sentence.

the two sentences should logically entail each other.


Therefore, our model has the ability to utilize both lexical and non-lexical features naturally.

our framework contains two primary components: • A neural network-based dependency parser.


Given a sentence-aligned parallel corpus D, we first conduct unsupervised bidirectional word alignment, and then collect an alignment dictionary.

in each word-aligned sentence pair of D, we keep all alignments with conditional alignment probability exceeding a threshold δ = 0.95 and discard the others.


Specifically, in each word-aligned sentence pair of D, we keep all alignments with conditional alignment probability exceeding a threshold δ = 0.95 and discard the others.

let .., N S } be the alignment dictionary, where c i,j is the number of times when the i th target word w T i is aligned to the j th source word w S j .


Therefore, in order to improve the robustness of projection, we utilize a morphology-inspired mechanism, to propagate embeddings from in-vocabulary words to out-ofvocabulary (OOV) words.

for each OOV word w T oov , we extract a list of candidate words that is similar to it in terms of edit distance, and then set the averaged vector as the embedding of w where To reduce noise, we choose a small edit distance threshold τ = 1.


For the relation-specific words for each relation (except Other), we use words (see Table 1) derived mainly from Marcu (1997)'s list of discourse markers.

type 4 (φ 4 ) the final set of features (used only in model 3) are present to help the model learn connections between the words in the question and the relation type r. we check if the interrogative word in the question matches the class represented by r. For instance, the word why matches the Causal relation.


The set of FEs is further split into core FEs and non-core FEs.

the core FEs are the essential components of a frame and can be defined by themselves.


Depending on the interpretation type of the omission, Chinese FrameNet divides the NIs into two categories: 1) Indefinite Null Instantiations (INIs), the missing element which can be understood given interpretational conventions and do not need resolution, and 2) Definite Null Instantiations (DNIs), the missing element which is something that can be understood in the linguistic or discourse context, and the fillers need to be inferred from the context through resolutions.

in example (1), lexical unit (or target) launched/u evokes the semantic frame Cause_motion, which has nine core FEs, namely Agent, Theme, Source, Path, Goal, Area, Cause, Result, Initial_State, but only one of them is instantiated, i.e.


This is because Frame-to-Frame relations and FE-to-FE relations can provide relevant information for finding DNI filler among candidate frame element contents.

for one frame f 1 that contains a DNI, firstly we need to find related frame f 2 with it from context.


For each word type w ∈ [n], there is a unique hidden state H(w) ∈ [m] such that o(w|H(w)) > 0 and o(w|h) = 0 for all h = H(w).

this model is an HMM in which observation states are partitioned by hidden states.


Let v(w) ∈ R m be the wth row of O normalized to have unit 2-norm: A crucial aspect of this representational scheme is that its correctness is invariant to scaling and rotation.

clustering the normalized rows of diag(s)O a diag(s 2 )Q where O a is any element-wise power of O with any a = 0, Q ∈ R m×m is any orthogonal transformation, and s 1 ∈ R n and s 2 ∈ R m are any positive vectors yields the correct clusters under the model.


The square-root transformation is a variancestabilizing transformation for Poisson random variables (Bartlett, 1936;Anscombe, 1948).

the square-root of a Poisson variable has variance close to 1/4, independent of its mean.


The CCA scaling, combined with the square-root transformation, gives the best overall performance.

it performs better than all other methods in mixed analogy questions by a significant margin.


In general, using any of these lexical features provides substantial improvements over the baseline.

6 the 30-dimensional CCA embeddings improve the F1 score by 2.84 on the development portion and by 4.88 on the test portion.


As K-B hierarchies are large complex DAG structures, we develop a highly-efficient scheme to enable practical training.

we associate a separate distance metric M h ∈ R n×n with each category h in the hierarchy.


We propose an efficient approach that avoids enumerating paths and instead models the underlying nodes directly.

we extend P e,e as the set of all category nodes included in any of the e → e paths, and define the aggregate metric as M e,e = γ e,e h∈P e,e π ee ,h M h , where {π ee ,h } are the relative weights of the categories such that h∈P e,e π ee ,h = 1.


For efficiency, we use negative sampling to reformulate the training objective, which is then optimized through coordinate gradient ascent.

given the training data {(e T , e C )} extracted from KB corpora, the representation learning is formulated as maximizing the objective in Eq 1, subject to PSD constraints on distance metrics M h 0, and v e 2 = v e 2 = 1 to avoid scale ambiguity.


Our algorithm finds the optimal configuration for the mentions of a document by maximizing the overall relatedness among assigned entities, together with the local mention-to-entity compatibility.

we first construct a mention-toentity dictionary based on Wikipedia annotations.


We apply a similar category matching strategy as in previous work (Chen et al., 2014b) that assesses lexical (e.g., head words) similarity between category names, while replacing the text matching with entity relatedness measure.

we first extract the underlying entities mentioned in Q through entity linking, then score each candidate entity by its average relatedness to the entities in Q.


To validate the advantage of incorporating hierarchical structure, we design a baseline that relies on entity embedding without entity hierarchy.

we obtain entity vectors by fixing the distance metric in Eq 4 as an identity matrix.


They found that semantic differences, such as between big and small, and also syntactic differences, as between big and bigger, were encoded consistently across their space.

they solved the word-analogy problems by exploiting the fact that equivalent relations tended to correspond to parallel vectordifferences.


Our experiments here are based on models that construct vector-representations within a model that predicts the occurence of words in context.

we focus on the CBOW and Skip-gram models of Mikolov etal.


However, knowing that opposite sides of the square in Figure 1 are parallel to each other still leaves open the question of what happens at the corners.

what is the relationship between the semantic differences, e.g.


Each node in the tree then requires both a bag vector, n b , to handle the bag context, and 2k sequence vectors, n s i , to handle the sequence context vectors, with probabilities given by: A second source of information which might be used to differentiate semantic from syntactic representations is morphology.

english has the useful characteristic that the written words themselves can often be broken into a semantic stem on the left and a syntactic ending on the right.


Finally, we want to incorporate all these elements in a single model, with the morphological and word order elements of the model working in harmony.

we want the sequential part of the model to be guided by morphological information without being constrained to give all words with same ending the same representation.


It seems feasible that our decomposition of representations could benefit tasks that need to differentiate their treatment of semantic and syntactic content.

applications of word embeddings that mainly involve syntax, such as POS tagging (e.g., Tsuboi, 2014) or supertagging for parsing (e.g., Lewis and Steedman, 2014), may be a reasonable starting point.


The representation power of the semantic parse is thus controlled by the set of legitimate actions applicable to each state.

we stage the actions into three main steps: locating the topic entity in the question, finding the main relationship between the answer and the topic entity, and expanding the query graph with additional constraints that describe properties the answer needs to have, or relationships between the answer and other entities in the question.


An action grows a given graph by adding some edges and nodes.

a e picks an entity node; a p determines the core inferential chain; a c and a a add constraints and aggregation nodes, respectively.


5 shows three possible chains that expand the single-node graph in s 1 .

because the topic entity e is given, we only need to explore legitimate predicate sequences that can start from e. to restrict the search space, we explore all paths of length 2 when the middle existential variable can be grounded to a CVT node and paths of length 1 if not.


Our semantic parsing model defines a distribution over logical forms given by the domaingeneral grammar G and additional rules triggered by the input utterance x.

given an utterance x, we detect numbers, dates, and perform string matching with database entities to recognize named entities.


However, this research only focuses on how the mood information from social media can be used to predict the stock price movement.

the mood of topics in social media is extracted by sentiment analysis.


It will sequentially sample hidden variables z t d,m and z o d,m from the distribution over these variables given the current values of all other hidden and observed variables.

in order to perform Collapsed Gibbs Sampling, conditional probability , w, c) is calculated by marginalizing out random variables Φ b , Φ t , Φ o , θ t and θ o .


This paper focuses on prediction of not the stock price but movement of it.

our goal is to develop a model that predicts if the stock price goes up or down.


The composition function becomes where t i is the phrase tag for node i, W t i and b t i are the parameters of function g t i , as defined in Equation 2.

phrase nodes with various tags have their own composition functions such as g N P , g V P , and so on.


We propose to use a feature-rich discriminative parser, and to learn the parameters of this parser using a convex quadratic objective function.

this approach also allows us to induce non-projective dependency structures.


Compared to the gradient descent algorithm, the Frank-Wolfe alogrithm does not take a step in the direction of the gradient, but in the direction of the point that minimizes the linear approximation of the function f over the convex set D (see Fig 3).

this ensures that the points z t always stay inside the convex set, and there is thus no need for a projection step.


Although the CCG derivations and dependencies that CCG-based parsers return should in principle be amenable to a quantitative labeled evaluation when a gold-standard CCG corpus is available, there may be minor systematic differences between the sets of categories assumed by the induced parser and those in the treebank.

the lexical categories in the English CCGbank are augmented with morphosyntactic features that indicate e.g.


We use imitation learning to set the parameters θ e of our agent by training it to classify whether a particular action is the one an expert policy would take in the current state.

we use θ e as parameters for a binary logistic classifier that predicts which action (merge or do not merge) matches the expert policy.


We can exploit this with memoization, significantly improving the algorithm's runtime.

we store the following values: • Given a state s and action a, the value of the cost function, r(s, a).


Inspired by recent work in learning representations for natural language tasks (Collobert et al., 2011), we explore neural network models which take only raw, unconjoined features as input, and attempt to learn intermediate representations automatically.

the model we describe attempts to create independent feature representations useful for both detecting the anaphoricity of a mention (that is, whether or not a mention is anaphoric) and ranking the potential antecedents of an anaphoric mention.


We consider in particular the scoring function: where h a and h p are feature representations, nonlinear functions of the features φ a and φ p (respectively), and g is a function of these representations.

we define and we take g to either be the identity function, in which case the above model is analogous to s lin+ but defined over non-linear feature representations, or to be an additional hidden layer: g( hp(x,y) + b).


The proposed pre-training scheme involves learning the parameters associated with h a and h p using two natural subtasks: anaphoricity detection and antecedent ranking.

we (1) train h a on the task of predicting whether a particular mention is anaphoric or not, and (2) train h p on the task of predicting the antecedent of mentions known to be anaphoric.


5 Formally, let t n ∈ {−1, 1} indicate whether ∈ C (x n ) or not (respectively).

t n = 1 if and only if x n is anaphoric.


We chose to base our coreference resolvers on this last class of methods, which permit efficient decoding by shifting from entity clusters to latent coreference trees.

the inclusion of lexicalized features by  yields nearly state-of-the-art performance with surface information only.


The noticeable difference is that now both partition functions include extra cost terms, either task-specific ( (y, Y(E)) in Z ) or with soft constraints (u g(x, y) in Z u ).

if we set a single constrained feature g 1 (x, y) := I( (y, Y(E)) = 0) with weight u 1 → +∞, all non-zero-cost summands in Z u (w, x) Figure 2: Comparison of saddle-point and penalized PR for Spanish, using the setup in §5.5.


To generate each token w d,n of a speech d, legislator a d will (1) first choose an issue z d,n ∈ [1, K] from a document-specific multinomial θ d , then (2) choose a frame t d,n from the set of infinitely many possible frames of the given issue z d,n using the frame proportion ψ d,k drawn from a Dirichlet process, and finally (3) choose a word type from the chosen frame's topic φ z d,n ,t d,n .

our model generates speeches using a mixture of K HDPs (Teh et al., 2006).


In this paper, we propose an unsupervised model that learns a latent hierarchical structure of categories from an input corpus, learns latent semantic relations between categories, and also identifies facts from the corpus that match the learned structure.

the model learns both the schema for a KB, and a set of facts that are related to that schema, thus combining the processes of KB population and ontology construction.


(2007) extended the LDACOL model to generate the second word of a collocation from a distribution that conditions on not only the first word but also the first word's topic assignment, proposing the topical N-gram (TNG) model.

whereas LDACOL only adds a distribution for every word-type to LDA, TNG adds a distribution for every possible word-topic pair.


Recent works on semantic parsing for question answering operate on more open and diverse data domains.

large-scale knowledge bases have gained popularity in the semantic parsing community (Cai and Yates, 2013;Berant et al., 2013;Fader et al., 2014).


Consider an s-component representation C = (C, φ) in SG and let E be the set of all edges that are adjacent to a source node in VS C and contained in an s-component in C. Then we let the boundary representation (BR) β of C in the s-graph SG be the pair β = (E, φ).

β represents the s-components through the in-boundary edges, i.e.


During parsing, such queries will be generated by the generic part of the parsing algorithm.

we will show how to answer the following types of query: • Top-down: given an s-component representation C of some s-graph and an algebra operation o, enumerate all the rules C → o(C 1 , .


However, they still suffer from some major limitations.

these corpus-based methods usually fail to capture the precise meanings for many words.


In this work, we use the popular Wikipedia corpus as our training data to learn word embeddings for experiments on the word similarity task and the TOEFL synonym selection task.

we utilize two Wikipedia corpora with different sizes.


One concrete definition that is sometimes used for paraphrases requires that they be bidirectionally entailing (Androutsopoulos and Malakasiotis, 2010).

in terms of RTE, it is assumed that if P is a paraphrase of Q, then P entails Q and Q entails P. In reality, paraphrases are often more nuanced (Bhagat and Hovy, 2013), and the entries in most paraphrase resources certainly do not match this definition.


In this paper, we show how a clear concept of semantics can be applied to large-scale paraphrase resources.

the entailment relations given by natural logic are a great fit for paraphrase resources, since natural logic operates on pairs of natural language expressions (like the entries in PPDB).


We emphasize that Propositions 1-2 hold without blowing up the number of symbols.

the dependency label alphabet is exactly the same as the set of phrasal symbols in the constituent representations.


For example, if the top element on the stack is NN, UNARY(NP) changes it to NP by applying the rule NP → NN.

this causes inconsistency in the numbers of actions between derivations (Zhu et al., 2013), which makes it hard to apply the existing best first search for dependency grammar to our system.


Sagae and Lavie ascribe the empirical success of their BFS to the sparseness of the distribution over subsequent actions in the MaxEnt model.

bFS is very efficient when only a few actions have dominant probabilities in each step, and the Max-Ent model facilitates this with its exponential operation (2).


5 Unary Merging Though this technique solves the problem with the structured perceptron for a simpler shift-reduce system, say for dependency grammar, the existence of unary actions, as mentioned in Section 2.1, requires additional effort in order to apply it to constituent parsing.

constituent parsing takes different numbers of SH state p: actions for each derivation, which means that the scores of two final states may contain different offset values.


The two kinds of dependency are coherent but not necessarily the same.

an adjunct is a syntactic dependent but usually a semantic predicate of its syntactic head.


To this end, we propose tree approximation to obtain CCG sub-graphs under the factorization using tree parsing algorithms.

we introduce an algorithm to associate every graph with a projective dependency tree, which we call weighted conversion.


Our factorization parser (with best experimental setting) does not utilize a grammar but do use syntactic information in the dependency formalism.

the parser extracts the so-called path features from dependency trees.


We therefore turn to ordersensitive sequential or tree-structured models.

tree-structured models are a linguistically attractive option due to their relation to syntactic interpretations of sentence structure.


The Constituency Tree-LSTM outperforms existing systems on the fine-grained classification subtask and achieves accuracy comparable to the state-of-theart on the binary subtask.

we find that it outperforms the Dependency Tree-LSTM.


The weight larger than 1 is given to the concept occurrences in the first few paragraphs.

the weight of the first paragraph is B and the weight decreases as the position of the paragraph increases from the beginning of the doc- 1 We only consider the recursive operation for a VP with more than one parallel sub-VPs, such as the highest VP in Figure 1.


We adopt those resolution rules that are able to achieve high quality and address our need for summarization.

sieve 1, 2, 3, 4, 5, 9, and 10 in the package are used.


To find alternative VPs, Jaccard Index is employed as the similarity measure.

each VP is represented as a set of its concepts and the index value is calculated for each pair of VPs.


The notations are summarized in Table 1.

we maximize the salience score of the selected NPs and VPs as indicated by the first and the third terms in Equation 3, and penalize the selection of similar NP pairs and similar VP pairs as indicated by the second and the fourth terms.


(2) To simulate compression-based summarization, we can adapt our framework to conduct sentence selection and sentence compression in a joint manner.

we only need to restrict that the NP and VPs of a summary sentence must come from the same original sentence.


The automated pyramid scoring procedure relies on distributional semantics to assign SCUs to a target summary.

all n-grams within sentence bounds are extracted, and converted into 100 dimension latent topical vectors via a weighted matrix factorization model (Guo and Diab, 2012).


In general, ignoring the grammaticality scores, our system still performs better than System 22.

the average scores of our system and System 22 on the last four questions are 3.37 and 3.33 respectively.


Note that, (I • M ) in most case is not stochastic and must not be transformed into a stochastic transitional matrix, as the transformation will collapse the global context of I. IRW is different to PageRank on weighted graph, weighted or personalized PageRank and their variations e.g, (Xing and Ghorbani, 2004;Haveliwala, 2002), among others.

weighted PageRank integrates influence scores into the stochastic transitional matrix.


Interestingly, removing the basic features leads to an increase in both unigram and bigram precision; in the bigram case this is enough to cause a statistically significant increase in F-measure over the full model.

the generic features actually lead to an inferior model when we can incorporate more appropriate domain specific features.


In this paper we propose a framework for automatic analysis of a large collection of political texts.

we demonstrate how the use of Bayesian methods and time series analysis captures the different ways in which political parties control the political discourse.


The model without good estimates of unseen referents always assigns probability to unseen referents in the denominator of (5), regardless of whether the word is a proper name or pronoun.

this model does not have good estimates of unseen referents like the complete model does.


We reveal that subtle signs of imminent betrayal are encoded in the conversational patterns of the dyad, even if the victim is not aware of the relationship's fate.

we find that lasting friendships exhibit a form of balance that manifests itself through language.


In this work, we provide a framework for analyzing a dyad's evolving communication patterns and provide evidence of subtle but consistent conversational patterns that foretell the unilateral dissolution of a friendship.

imminent betrayal is signaled by sudden changes in the balance of conversational attributes such as positive sentiment, politeness, and structured discourse.


In this paper, we propose a more generalized task setting for public surveillance.

our objective is to estimate the subject (carrier) of a disease or symptom mentioned in a Japanese tweet.


We verify the possibility of transferring supervision data to different targets of diseases and symptoms.

we verify that it is possible to utilize the supervision data for a particular disease/symptom to improve the accuracy of predicting subjects of another disease/symptom.


In this study, we annotated the subjects of diseases and symptoms in the corpus described in Section 2.1.

we annotated the subjects in 500 tweets for each disease/symptom (except for sore throat).


When the subject of a disease/symptom is FIRSTPER-SON, only 3.3% of the tweets have explicit textual clues for the first person 3 .

when the subject of a disease/symptom is FIRST-PERSON, we rarely find textual clues in tweets.


This is because the subjects for these symptoms are mostly FIRSTPERSON, as we seldom mention the symptoms of another person in such cases.

the episode classifier can predict a positive label for these symptoms without knowing the subjects of these symptoms.


The number of students in the j-th team is denoted as N j , (1 ≤ j ≤ N j ).

there are K roles across C teams that we want to identify, where the number of roles is smaller than or equal to the number of students in a team, which means that each role should have one student assigned to it, but not every student needs to be assigned to a role.


Here, we use K-Means method for clustering.

each cluster is a latent representation of a role and each student is assigned to its closest cluster (role).


In Table 1, we compare a variety of DAN and NBOW configurations 5 to the baselines described above.

we are interested in not only comparing DAN accuracies to those of the baselines, but also how initializing with pretrained embeddings and restricting the model to only root-level labels affects performance.


Following the work ofİrsoy and Cardie (2014), we examine our network by measuring the response at each hidden layer to perturbations in an input sentence.

we use the template the film's performances were awesome and replace the final word with increasingly negative polarity words (cool, okay, underwhelming, the worst).